Covarying neighborhood analysis is a method for finding structure in- and conducting association analysis with multi-sample single-cell datasets. cna does not require a pre-specified transcriptional structure such as a clustering of the cells in the dataset. It aims instead to flexibly identify differences of all kinds between samples. cna is fast, does not require parameter tuning, produces measures of statistical significance for its association analyses, and allows for covariate correction.
cna is built on top of scanpy and offers a scanpy-like interface for ease of use.
To use cna, you can either install it directly from the Python Package Index by running, e.g.,
pip install cna
or if you'd like to manipulate the source code you can clone this repository and add it to your PYTHONPATH.
Take a look at our tutorial to see how to get started with a small synthetic data set.
If you use cna, please cite
[Reshef, Rumker], et al., Axes of inter-sample variability among transcriptional neighborhoods reveal disease-associated cell states in single-cell data. BioRxiv, 2021. [...] contributed equally